2ADS Autonomous Drone Swarms

Authors: Abinaya Rajesh, Joe Mattekatt, Madhav Datt

Autonomous Drone Swarms

Roadmap Overview

Autonomous drone swarms are a collection of unmanned aerial vehicles (UAVs) that collaborate to perform a task with minimal human intervention. These drones take some information from a ground control station to outline the task but mainly rely on their own autonomous decision making to perform that task and synchronize with other drones in the swarm. Swarms can range from a few to thousands of drones depending on the application and the maturity of the technology used in these drones. Drone swarms have a variety of applications from military to aerial light shows. The focus of this research will be the technological advancements that have enabled the development of drone swarms and a prediction of where this technology will go next.

DSM Allocation

Autonomous Drone Swarms can be broken down into the following technologies that have some inter-dependencies.

1SR Swarm Robotics

2ADS Autonomous Drone Swarms

2D Drones

3BF Body Frame

4CF Carbon Fiber

3PS Propulsion System

4P Propellers

4BM Brushless Motors

3FCS Flight Control System

4IMS Inertial Measurement Sensors

4GPS GPS

4A Autonomy

4SA Swarm Algorithms

3ES Electric System

4IC Integrated Circuits

4RTR Radio Transmitter & Receiver

4LIO Lithium Ion Batteries

OPM Model

Figures of Merit (FOM)

FOM	Units	Significance
Swarm Size	[number of drones]	The number of drones in the swarm dictate the size and complexity of the light display that can be created. Larger swarms also require more sophisticated control software.
Endurance	[min]	The battery life of a drone determines how long an aerial show can last.
Speed	[miles/hour]	Determined by motor. Decides if fit for certain use cases.
Range	[km]	Specifies scope of operation and geographical coverage before. Computed as Flight Time * 60 * Speed.
Cost	[US Dollars]	Cost over overall system including per unit drone cost.
Latency	[ms]	How quickly a drone can respond to commands from a control center or react to signals from other drones. Lower latency systems will be safer.

Strategic Drivers

Number	Strategic Driver	Alignment and Targets
1	To develop drones capable of operating in a swarm to accomplish field tasks that would be too challenging for individual drones to accomplish.	The 2ADS technology roadmap will target drones acting together to form swarms that can accomplish tasks. Based on the Moore’s law target set in Assignment 2, the goal is to have swarm sizes of 500 by 2028.
2	To create lower cost drones so that customers buy a larger volume of drones instead of spending more on individual drones.	The 2D technology roadmap will benefit from cost saving developments in any of the component level roadmaps.
3	To develop autonomy that will enable drones to operate in locations that are not conducive to receiving human control signals.	The 3FCS technology roadmap will target improvements in autonomy that will initially allow a human to control multiple drones (Anduril Lattice) then eventually allow the drone to operate on its own.
4	To increase endurance of drones so that they can perform more complex tasks that take longer to complete.	The 3PS and 4LIO technology roadmaps will focus on improving propulsion efficiency and battery capacity (transition to solid-state batteries) which will enable longer endurance limits.
5	To increase the payload capacity of drones so that end effectors on drones can be more robust.	Improvements made in the propulsion efficiency and battery capacity of the 3PS and 4LIO technology roadmaps will also enable the drone to carry a larger payload if the endurance is kept constant.

Company Comparison

Drone Name	Range [km]	Endurance [min]	Mass [kg]
Anduril Bolt	20	45	5.44
Huntress Turbojet	30	120	50
DJI Matrice 300 RTK	15	55	6.3
Skydio X10	10	35	2.5
Parrot ANAFI USA	4	32	4.5
Freefly Alta X	5	40	11

In order to move the pareto front higher, technological improvements need to be made on battery capacity and efficiency of the overall drone system. A hypothetical company attempting to move this front would be an attacker in this space. Currently, the existing companies are trying to get to the existing pareto front taking on a fast follower strategy.

Technical Model: Morphological Matrix

Analyzing the morphological matrix of the drones at the pareto frontier, we see design convergence with regard to battery type, rotor count and control type. The differences that contribute to variations in the endurance and range FOMs are mainly determined by battery capacity, mass and payload. The DJI Matrice does well with its relatively smaller battery capacity yet still having an endurance limit on the pareto front.

For the purpose of this technical analysis, we will look at the governing equations of a multi-rotor drone swarm doing surveillance. Since the analysis for each drone in the swarm would be similar, we will specifically analyze the equations related to the hovering characteristics of these drones using an Anduril Bolt military drone as a reference point.

We can observe that the endurance is affected by the battery capacity, efficiency, rotor radius, number of rotors and mass of the drone.

Given the design vector of the Anduril Bolt Drone, here are the ways to increase endurance of the drone based on the magnitudes on the normalized tornado plot:

• Making a 1% decrease in the mass will have the greatest effect in increasing endurance by 1.5%.
• Making a 1% increase in the rotor radius, efficiency or battery capacity will increase endurance by 1% however if this increases the mass of the drone, some negative effects will be encountered.
• Making a 1% increase in Number of Rotors will increase the endurance by 0.5% however, this sort of change needs to be in whole rotor increments.

Given the design vector of the Anduril Bolt Drone, here are the ways to increase range of the drone based on the magnitudes on the normalized tornado plot:

• Making a 1% increase in the efficiency or battery capacity will increase range by 1% however if this increases the mass of the drone, some negative effects will be encountered.
• Making a 1% decrease in the mass will increase endurance by 0.99%.

Notably, the rotor parameters disappear from the range calculations because the optimal velocity is being fixed for the analysis based on empirical results.

Key Patents

Patent 1: Pillai, Unnikrishna Sreedharan, and Alain Anthony Mangiat. "Method and apparatus for dynamic swarming of airborne drones for a reconfigurable array." U.S. Patent No. 9,488,981. 8 Nov. 2016.

This patent introduces a novel method for controlling drone swarms using a hierarchical control system combining ground-based mission planning with autonomous drone coordination. The key innovation lies in its dual virtual region approach - each drone creates an inner "bubble" region (5-15x drone size) and a larger communication sphere (several hundred times larger) around itself. The system uses a unique centroid-based control mechanism where the swarm must maintain its calculated center point (centroid) within a defined sphere while individual drones have flexibility to move.

Relevant primary CPC classification: G05D1/0027 Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement involving a plurality of vehicles, e.g. fleet or convoy traveling

Patent 2: Husain, Syed Mohammad Amir, and John Rutherford Allen. "Stackable unmanned aerial vehicle (UAV) system and portable hangar system therefor." U.S. Patent No. 10,322,820. 18 Jun. 2019.

This Boeing patent presents an autonomous aerial refueling system for unmanned aerial vehicles (UAVs). The system utilizes an optical positioning mechanism that combines laser beams and retroreflectors to execute mid-air refueling operations. The technology enables automated guidance of multiple receiver UAVs through refueling procedures using laser beams from the tanker aircraft that interface with retroreflective markers on receiving vehicles. This system removes manual operation requirements while maintaining positioning accuracy during refueling maneuvers. The implementation incorporates computer vision algorithms, boom control systems, and retroreflective markers to facilitate positioning and fuel transfer between aircraft. The patent demonstrates advancement in autonomous aerial operations, with applications in UAV swarm refueling scenarios.

Relevant primary CPC classification: B64F1/222 Ground or aircraft-carrier-deck installations for handling aircraft for storing aircraft, e.g. in hangars

Patent 3: Steele, Daniel W., and Joseph R. Chovan. "Unmanned air vehicle, integrated weapon platform, avionics system and control method." U.S. Patent No. 7,542,828. 2 Jun. 2009.

This patent introduces a novel design methodology for an interceptor unmanned aerial vehicle (UAV) that integrates weapons capabilities with autonomous control systems. The key innovation lies in the design approach of building the UAV airframe around a weapon platform, specifically aligning the weapon's aiming axis with the aircraft's flight vector axis, essentially creating a "self-aiming winged weapon". The system incorporates a unique spherically-organized situational awareness database that processes sensor data in its native angular format, avoiding the computational complexity of Cartesian coordinate transformations. This vehicle-centric, attitude-invariant database enables parallel processing of target tracking and obstacle avoidance through independent "windowing" of the spherical data structure. The design also features innovative integration of major torque-inducing components (weapon, engine, wings) near the center of gravity, along with symmetric aerodynamic design using forward canards, which together enhance maneuverability and weapon accuracy while minimizing discharge-induced flight disturbances.

Relevant primary CPC classification: B64U2101/15 UAVs specially adapted for particular uses or applications for conventional or electronic warfare

Finally looking at patents in this space, we see the following CPC classifications overall for the technology autonomous drone swarms:

• B64U: vehicles which are specially adapted for unmanned aeronautical use and the equipment therefor
• G05D 1/104: For autonomous control and navigation
• G08G 5: For air traffic control systems associated with autonomous drones and drone swarms

Financial Model

Anduril is one of the companies at the forefront of drone swarm development. Their Lattice software allows different drones and other autonomous agents to communicate and operate together to perform a military task. This technology as a whole has the potential to generate lots of positive free cash flow since the operating model of drone swarms allows it to have different business model dynamics than the traditional cost plus model of defense companies. For example, the cost of goods sold (65%) is lower for military drone swarms since drones are made from commodity materials. Drones being unmanned also reduces complexity. Since autonomous software is a major component of this business, in the long run, the operating costs will be similar to those of a software-as-a-service company (20%). The defense spending is expected to continue increasing at 3.5% annually which will become the perpetual free cash flow growth rate for Anduril. To accelerate growth, the company plans to invest over $1.5 billion by 2026. Based on these projections, the company has a net present value of roughly $14 billion which aligns with its current valuation.

Givens:

• COGs is 65% of sales
• Operating Expenses is 20% of sales in long run
• Military budget increases at 3.5% annually
• Capital Expenses will be an additional $1.5 billion by 2026
• R&D Expenses are captured into COGs and CapEx and prorated to each unit sold